Machine vision systems for intelligent mobile platforms have been proposed in the past as a possible mechanism for object avoidance, object identification and mobile platform navigation. Typically, the systems utilize complicated components which attempt to calculate the distance from the mobile platform to an object and/or features of an object based on relative movement of the object through an array of pixels or voxels captured by a multi modal data capture device or sensor. At least in some cases, the mobile platforms consist of a computing engine, a plurality of physical sensors (i.e., gyroscopes, accelerometers, Global Positioning System (GPS) devices) and sensors that can form images or retrieve positional data (i.e., camera, lidar (light detection and ranging) sensors, etc.). Additionally, these mobile systems can have a plurality of electro-mechanical actuators that need to be controlled and may run one or more machine vision applications on the computing engine of the mobile platform. Typical image processing tasks performed by these machine vision applications include, but are not limited to, recording video, building mosaics, tracking features in the image, geo-location of the image and building 3D “world models” of the space surrounding the system. Typical control operations include, but are not limited to, navigating this 3-D space, performing tasks the system has been designed to perform, actively stabilizing the sensors for the best possible measurements and synchronizing active mobile platform controls with sensor data.
A majority of the aforementioned machine vision data processing tasks are computationally expensive, require high power, are error prone, difficult to program and difficult to run in real time. However, it is noted that the faster the mobile platform can move the more precise positioning data needs to be. Precision of the individual system components is typically a linear function with respect to timing uncertainty. The linear errors of individual components contribute to system errors in complex ways, typically, random, chaotic and nonlinear. Accordingly, deterministic timing is of paramount importance.